Results 71 to 80 of about 13,817 (251)

A New Localization Algorithm Based on Taylor Series Expansion for NLOS Environment

Cybernetics and Information Technologies, 2016
In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion location algorithm based on self-adaptive Radial-Basis-Function (RBF) neural network is proposed in ...
Ren Jin, Chen Jingxing, Bai Wenle
doaj   +1 more source

Daily Residential Natural Gas Demand Forecasting Using Machine Learning Regression: Comparative Evaluation With a Case Study in Qazvin Province, Iran

Energy Science &Engineering, EarlyView.
This graphical abstract summarizes the proposed framework for improving short‐term residential natural gas consumption forecasting by integrating a novel socioeconomic indicator, the subscription growth ratio (SGR), with conventional meteorological variables.
Ali Pirzad, Mostafa Khanzadi
wiley   +1 more source

RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

IEEE Access, 2017
Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task.
Pengcheng Zhang, Xuewu Zhou, Patrizio Pelliccione, Hareton Leung   +3 more
doaj   +1 more source

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source

Phase transmittance RBF neural networks

Electronics Letters, 2007
Presented is a new complex valued radial basis function (RBF) neural network with phase transmittance between the input nodes and output, which makes it suitable for channel equalisation on quadrature digital modulation systems.
D.V. Loss, M.C.F. DeCastro, P.R.G. Franco, F.C.C. DeCastro   +3 more
openaire   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Dopamine Transporter Imaging‐Based Radiomics for Predicting 4‐Year Motor Progression (Unified Parkinson's Disease Rating Scale Part III) in Parkinson's Disease

iRADIOLOGY, EarlyView.
Magnetic resonance imaging (MRI)‐guided dopamine transporter (DAT) radiomics combined with machine learning showed promising performance for predicting 4‐year motor progression in Parkinson's disease. Ensemble voting fusion markedly improved discrimination compared with individual base models, with stable predictive features mainly derived from ...
Xiaoxuan Fan, Yanghan Chen, Junfeng Lin, Zenwen Han, Jianqiang Ye, Lili Lin, Hong Zhang, Ang Li, Han Jiang   +8 more
wiley   +1 more source

Molecular Functions and Drug Prediction of MMP1 and TGFBR2 in Nasopharyngeal Carcinoma

Journal of Clinical Laboratory Analysis, EarlyView.
MMP1 and TGFBR2 were validated by machine learning and external datasets as the candidate genes. Dexamethasone showed strong binding affinity to MMP1 (−7.72 kcal/mol) and TGFBR2 (−7.27 kcal/mol) in docking studies. Knockdown of MMP1 repressed the malignant phenotypes of NPC cells.
Feng Wang, Binbin Ge, Ming Li, Xiaochun Xia, Fuming Zhang, Pengqin Xu   +5 more
wiley   +1 more source

Molecular descriptor driven QSPR modeling of Papp, TEER and Efflux Ratio from Caco‐2 cells using machine learning for various phytochemicals

Journal of the Science of Food and Agriculture, EarlyView.
Abstract BACKGROUND The present study aimed to develop and validate quantitative structure–property relationship (QSPR) models for predicting permeability related bioavailability indicators including apparent permeability (Papp), trans‐epithelial electrical resistance (TEER) and efflux ratio (ER) based on molecular descriptors (n = 5003) of 83 ...
Jin‐Woo Kim, Rixing Cong, Jin‐Soo Park, Keunwan Park, Kyungsu Kang, Soon‐Mi Shim   +5 more
wiley   +1 more source